Computational and Experimental Assessment of Backbone Templates for Computational Redesign of the Thioredoxin Fold.

Computational protein design has taken big strides in recent years; however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have applied here a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the thioredoxin fold. We used a genetic screening tool to estimate solubility/folding of the designed proteins in E. coli and to select the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state-of-the-art design software is now able to predict the best template, most likely due to the introduction of an energy term that reports on stress in covalent bond lengths and angles. The template that led to the greatest fraction of successful designs was the same (a thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of thioredoxin, which also used the spinach protein as a template, however also performed well as a template. In the present study, both of these templates yielded proteins with compact folded structures and enforced the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs based on energies appears to correctly identify such templates.

AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.

Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived from natural proteins show promise, as they are generally regarded as safe and potentially contain other beneficial bioactivities. Antioxidative peptides are usually obtained by testing various peptides derived from hydrolysis of proteins by a selection of proteases. This slow and cumbersome trial-and-error approach to identify antioxidative peptides has increased interest in developing computational approaches for prediction of antioxidant activity and thereby reduce laboratory work. A few antioxidant predictors exist, however, no tool predicting the antioxidative properties of peptides is, to the best of our knowledge, currently available as a web-server. We here present the AnOxPePred tool and web-server ( http://services.bioinformatics.dtu.dk/service.php?AnOxPePred-1.0 ) that uses deep learning to predict the antioxidant properties of peptides. Our model was trained on a curated dataset consisting of experimentally-tested antioxidant and non-antioxidant peptides. For a variety of metrics our method displays a prediction performance better than a k-NN sequence identity-based approach. Furthermore, the developed tool will be a good benchmark for future predictors of antioxidant peptides.

T cell receptor sequence clustering and antigen specificity.

There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.